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Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation.
Ritari, J; Hyvärinen, K; Koskela, S; Itälä-Remes, M; Niittyvuopio, R; Nihtinen, A; Salmenniemi, U; Putkonen, M; Volin, L; Kwan, T; Pastinen, T; Partanen, J.
Afiliação
  • Ritari J; Finnish Red Cross Blood Service, Helsinki, Finland. jarmo.ritari@bloodservice.fi.
  • Hyvärinen K; Finnish Red Cross Blood Service, Helsinki, Finland.
  • Koskela S; Finnish Red Cross Blood Service, Helsinki, Finland.
  • Itälä-Remes M; Turku University Hospital, Turku, Finland.
  • Niittyvuopio R; Helsinki University Hospital, Comprehensive Cancer Center, Stem Cell Transplantation Unit, Helsinki, Finland.
  • Nihtinen A; Helsinki University Hospital, Comprehensive Cancer Center, Stem Cell Transplantation Unit, Helsinki, Finland.
  • Salmenniemi U; Turku University Hospital, Turku, Finland.
  • Putkonen M; Turku University Hospital, Turku, Finland.
  • Volin L; Helsinki University Hospital, Comprehensive Cancer Center, Stem Cell Transplantation Unit, Helsinki, Finland.
  • Kwan T; McGill University, Montreal, Canada.
  • Pastinen T; McGill University, Montreal, Canada.
  • Partanen J; Children's Mercy Kansas City, Kansas City, MO, USA.
Leukemia ; 33(1): 240-248, 2019 01.
Article em En | MEDLINE | ID: mdl-30089915
ABSTRACT
Allogeneic haematopoietic stem cell transplantation currently represents the primary potentially curative treatment for cancers of the blood and bone marrow. While relapse occurs in approximately 30% of patients, few risk-modifying genetic variants have been identified. The present study evaluates the predictive potential of patient genetics on relapse risk in a genome-wide manner. We studied 151 graft recipients with HLA-matched sibling donors by sequencing the whole-exome, active immunoregulatory regions, and the full MHC region. To assess the predictive capability and contributions of SNPs and INDELs, we employed machine learning and a feature selection approach in a cross-validation framework to discover the most informative variants while controlling against overfitting. Our results show that germline genetic polymorphisms in patients entail a significant contribution to relapse risk, as judged by the predictive performance of the model (AUC = 0.72 [95% CI 0.63-0.81]). Furthermore, the top contributing variants were predictive in two independent replication cohorts (n = 258 and n = 125) from the same population. The results can help elucidate relapse mechanisms and suggest novel therapeutic targets. A computational genomic model could provide a step toward individualized prognostic risk assessment, particularly when accompanied by other data modalities.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Polimorfismo Genético / Biomarcadores Tumorais / Transplante de Células-Tronco Hematopoéticas / Neoplasias Hematológicas / Genômica / Doença Enxerto-Hospedeiro / Recidiva Local de Neoplasia Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged Idioma: En Revista: Leukemia Assunto da revista: HEMATOLOGIA / NEOPLASIAS Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Finlândia

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Polimorfismo Genético / Biomarcadores Tumorais / Transplante de Células-Tronco Hematopoéticas / Neoplasias Hematológicas / Genômica / Doença Enxerto-Hospedeiro / Recidiva Local de Neoplasia Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Male / Middle aged Idioma: En Revista: Leukemia Assunto da revista: HEMATOLOGIA / NEOPLASIAS Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Finlândia